kyllo

> You could say the same thing about compilers versus assemblers, high-level languages versus low-level ones, and services and libraries versus monolithic programs.

Right, but we didn't invest a trillion dollars of capital into any of those things in the span of a couple years, thus forcing them to capture value and show a return on such a massive investment.

On the value capture question, it's really not clear that the ability to write code at superhuman speed actually translates to an increase in the speed with which we can create new, better technology products that customers will pay more money for, vs. just enabling layoffs.

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Reads like fearmongering about oppressive and violent applications of AI that the Chinese government hypothetically could deploy, which the US government is already actively working on in the meantime.

Only perfect multicollinearity (correlation of 1.0 or -1.0) is a problem at the linear algebra level when fitting a statistical model.

But theoretically speaking, in a scientific context, why would you want to fit an explanatory model that includes multiple highly (but not perfectly) correlated independent variables?

It shouldn't be an accident. Usually it's because you've intentionally taken multiple proxy measurements of the same theoretical latent variable and you want to reduce measurement error. So that becomes a part of your measurement and modeling strategy.

In other words, as economists say, because OLS is provably the BLUE (Best Linear Unbiased Estimator) aka the Gauss-Markov Theorem.

You make a good point, though the difference between ML and statistics isn't just about interpreting and validating the model. It's about the "novel discoveries" part aka Doing Science.

Statistical modeling is done primarily in service of scientific discovery--for the purpose of making an inference (population estimate from a sample) or a comparison to test a hypothesis derived from a theoretical causal model of a real-world process before viewing data. The parameters of a model are interpreted because they represent an estimate of a treatment effect of some intervention.

Methods like PCA can be part of that modeling process either way, but analyzing and fitting models to data to mine it for patterns without an a priori hypothesis is not science.

This. I bought toothpaste on Amazon, used it up, and the next time I went to buy the exact same toothpaste, the price had doubled.

USPA has a tested division now and it's been gaining in popularity--it will soon be more popular than the untested division if it isn't already. Most of the top untested powerlifters have moved over to the WRPF (which does also have its own tested division). There are a lot of other smaller, regional, untested feds. Then there's the IPF and USAPL and their affiliates, which are fully tested, and are now far more popular than any of the untested feds. Untested might never go away, but tested has rapidly surpassed it in recent years.

Which is based not on your ability to produce value, but your ability to capture value and charge a cut of every unit, and is thus a massive disincentive to produce public goods.

When I lived in South Korea, one of the things that struck me was how much "flatter" the generations there were in terms of pop culture and music taste and awareness, compared to the US. I worked in an office with a bunch of suit-and-tie businessmen who were mostly in their 40s to 60s, and if you were to ask them about any current K-pop group, they all knew their hit songs.

My friend (who's the same age as me) has a 14 year old son who's learning guitar and he asked me for a lesson. The first thing he wanted me to show him was some riffs from AC/DC songs that came out before I was born.